Evaluation of anthropometric indices as a predictor of diabetes in Dong and Miao ethnicities in China: A cross-sectional analysis of China Multi-Ethnic Cohort Study

Background Although it is known that obesity is inseparable from diabetes, many anthropometric indices are used for determining obesity. At the same time, research on the predictive indices of diabetes in Chinese minority populations is lacking. Therefore, this study determines the relationship between different anthropometric indices and diabetes, and identifies the best index and best cut-off values for predicting diabetes. Method In total, 11,035 Dong and Miao ethnic participants (age: 30–79 years) from the China Multi-Ethnic Cohort study were included. The logistic regression model was used to examine the relationship between the different anthropometric indices and diabetes risk. The receiver operating characteristic curve and the area under the curve (AUC) were used to identify the best predictor of diabetes. Results In multivariate adjusted logistic regression models, body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), a body shape index (ABSI), body roundness index (BRI), and visceral adiposity index (VAI) were positively correlated with diabetes risk. Among Chinese Dong men and women and Miao men, WHR had the largest AUC (0.654/0.719/0.651). Among Miao women, VAI had the largest AUC(0.701). The best cut-off values of WHR for Dong men and women and Miao men were 0.94, 0.92, and 0.91, respectively. The best cut-off value of VAI for Miao women was 2.20. Conclusion Obesity indicators better predict diabetes in women than men. WHR may be the best predictor of diabetes risk in both sex of Dong ethnicity and Miao men, and VAI may be the best predictor of diabetes risk in Miao women.


Method
In total, 11,035 Dong and Miao ethnic participants (age: 30-79 years) from the China Multi-Ethnic Cohort study were included. The logistic regression model was used to examine the relationship between the different anthropometric indices and diabetes risk. The receiver operating characteristic curve and the area under the curve (AUC) were used to identify the best predictor of diabetes.

Results
In multivariate adjusted logistic regression models, body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), a body shape index (ABSI), body roundness index (BRI), and visceral adiposity index (VAI) were positively correlated with diabetes risk. Among Chinese Dong men and women and Miao men, WHR had the largest AUC (0.654/0.719/0.651). Among Miao women, VAI had the largest AUC(0.701). The best cut-off values of WHR for Dong men and women and Miao men were 0.94, 0.92, and 0.91, respectively. The best cut-off value of VAI for Miao women was 2.20. Introduction anthropometric indices and diabetes in the Chinese Dong and Miao ethnic groups and determined the best cut-off values for the corresponding indices to provide evidence for diabetes prevention.

Study population
The present study participants were from the China Multi-Ethnic Cohort (CMEC) study, which has been recounted elsewhere [19]. From July 2018 to August 2019, a multi-stage, stratified cluster sampling method was used to obtain samples from the community population aged 30-79 years. In the first stage, the Qiandongnan Prefecture, China was selected as our study sites. In the second stage, four communities (Liping County, Congjiang County, Kaili City and Leishan County) in the settlement were selected by the local Centres for Disease Control and Prevention (CDCs), taking into account migration status, local health conditions and, most importantly, ethnic structure. In the final stage, all participants who met our inclusion criteria were invited to participate in our studies in consideration of both sex ratio and age ratio. Participants completed an epidemiological questionnaire including questions on sociodemographics, lifestyle, and medical history at baseline and underwent physical examination. The data of Dong and Miao population were obtained from the CMEC Study database. A total of 12.798 people from the Dong and Miao ethnic group were selected from the database, the participants with fasting for less than 8 h, existing or previous tumours, lack of physical examination data, lack of blood biochemical examination data, and a history of organ removal were excluded. The final study population involved 11,035 Dong and Miao ethnic groups from Guizhou Province (Fig 1).

Data collection
First, a well-trained professional investigator conducted face-to-face interviews with the participants, which included questions about demographics, lifestyle, and medical history. Second, the fasting venous blood of the participants was collected by professional medical staff, and the samples were transported to Guizhou KingMed Diagnostics Group Co., Ltd for evaluating the levels of FPG, total cholesterol (TC), triglycerides (TG), high-density lipoprotein, and low-density lipoprotein (LDL-C) and glycated haemoglobin (HbA1c) by a fully automatic biochemical analyser (HITACHI 7180, Tokyo, Japan). Third, all survey participants underwent complete physical examination, which was conducted by trained medical professionals in accordance with standard procedures.

Definition
An individual is considered to have diabetes in case of the following: fasting blood glucose � 7.0 mmol/L; glycated haemoglobin � 6.5%, and having diagnosed previously as having diabetes by a secondary hospital or above [20]. A person is considered a smoker if he/she has smoked more than 100 cigarettes so far [19]. A person is considered an ever smoker if he/ she has quit smoking for more than half a year. BMI, WHR, WHtR, ABSI, BAI, BRI, and VAI were defined as follows [10,12,13,21]:

Statistical analysis
Continuous variables were described as mean ± SD, and categorical variables were described by frequency (percentage). The Mann-Whitney U test or chi-square test was used to evaluate the difference in indicators between different populations. After adjusting for age, smoking, drinking, educational level, hypertension, chronic hepatitis or hepatocirrhosis, coronary heart disease, hyperlipidaemia (except for VAI), and family history of diabetes, and grouping anthropometric indicators into tertiles, the logistic regression model was used to explore the association between anthropometric indices and diabetes, and its odds ratio (OR) and 95% confidence interval (CI) were calculated. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to identify the best predictor of diabetes. The inspection level was 0.05 (two-tailed). All statistical analyses were performed using IBM SPSS 25.0, Stata 15, and MedCalc 20.0.9 software. , and LDL-C were lower in both sexes of the Miao ethnic group (P < 0.05), whereas DBP, BMI, HC, WHtR, TC, BRI, and BRI were higher in both sexes (P < 0.001). The number of current or ever smokers and drinkers was higher among the Miao women than among the Dong women (P < 0.05). SBP and WC were higher, whereas TG, FPG, and VAI were lower in the Miao women than in the Dong women (P < 0.05). WHR was lower in the Miao men than in the Dong men (P < 0.05).   Table 3 presents the best cut-off values of the eight anthropometric indices, which were determined based on the largest Youden index. Among Dong men, WHR, whose best cut-off value was 0.94, had the largest AUC (0.654, 95% CI: 0.634-0.674), and its predictive performance was significantly higher than that of BMI, ABSI, BAI, and VAI (P < 0.05), whereas WC, WHtR, and BRI had predictive capabilities similar to that of WHR (S1 Table). Among the Dong women, except for WHtR and BRI, and the predictive power of the other indices was weaker than that of WHR and the best cut-off value was 0.92 (P < 0.05).

ROC analysis
Among the Miao men, the predictive ability of ABSI and BAI was weaker than that of WHR and the best cut-off value of WHR was 0.91. Among the Miao women, the AUC of VAI was the largest (0.701, 95% CI: 0.685-0.718) and was significantly greater than that of BMI, ABSI, and BAI (P < 0.05), and the best cut-off value of VAI was 2.20. . In addition, two cohort studies in China have found that BMI is the best predictor of diabetes risk in Chinese adults [7,8]. However, Nevil et al. proposed that whether BMI can accurately represent obesity and can distinguish between obese and non-obese people remain to be investigated [2]. Subsequent studies have found that indices representing central obesity (WC and WHtR) predict fasting blood glucose, pre-diabetes, and diabetes better than BMI [3,26,27]. Hardy et al. revealed that WC is better than WHtR in predicting diabetes [9]. Skogberg showed that although WC and WHtR are the best indices for detecting diabetes, WHtR considers the height difference between individuals, and therefore is more reliable for diabetes risk assessment [28]. In China [6], Chile [29], and Jordan [30], studies have also shown that compared with WC and/or WHR, WHtR is the best anthropometric index for predicting diabetes. Our study revealed that compared with general obesity, central obesity (WC, WHR, and WHtR) has a better ability to distinguish diabetes from non-diabetes. Compared with non- also found that abdominal fat is significantly related to fasting blood glucose, insulin levels, and insulin resistance [15]. Through Mendelian randomisation analysis, Wang et al. found that abdominal obesity aggravates insulin resistance, leading to hyperglycaemia, which confirmed the potential causality between them [32].
Although traditional obesity indices such as WC can represent abdominal obesity, they cannot distinguish between VAT and SAT [13]. VAT has a stronger correlation with FPG than SAT [33]. Visceral obesity not only increases the risk of metabolic syndrome among adolescents but also increases the risk of cardiovascular disease in patients with diabetes [34, 35]. VAT is associated with diabetes in Chinese men and women, and its correlation is stronger than traditional anthropometric indices [36,37]. At present, the gold standard for distinguishing between VAT and SAT is imaging technologies, such as computed tomography (CT) and magnetic resonance imaging (MRI), but due to their shortcomings such as high cost and inconvenient to use, they cannot be used for diabetes screening in a large population. In 2010, Amato proposed that VAI can be used in daily clinical practice and population studies to assess the cardiometabolic risk associated with visceral obesity [13]. In 2014, Chen et al. showed that compared with other measurement indices, VAI is a more accurate and more convenient surrogate index for VAT measurement and can be used to identify diabetes risk in large-scale epidemiological studies [38]. A study showed that VAI is an independent predictor of T2DM in adults in Qatar [39]. Many studies have proven that VAI is related to diabetes risk in the Chinese population, but it does not perform better than traditional indices such as BMI, WC, and WHtR [8,40,41]. Liu et al. showed that VAI has a better ability to predict diabetes [42]. In our study, VAI, which was the best index for predicting diabetes in the Miao women, had the largest AUC. Abdominal fat accumulation varies with race, especially in VAT [14]. Body fat mass and belly fat content are higher in women than in men [15]. Therefore, we believe that Miao women may have higher visceral fat than the other three subgroups, but indices such as WC cannot distinguish between SAT and VAT. In our study, compared with WHR, WC, and WHtR, VAI had relatively higher sensitivity and specificity. Therefore, compared with WC, WHR, and WHtR, VAI has a better ability to predict diabetes risk in Miao women. In 2013, Thomas proposed a new anthropometric index, BRI, which is a predictor of body fat percentage and VAT [21]. Liu et al. also confirmed that BRI can be used as an effective index of visceral fat accumulation in patients with T2DM [43]. A study in northeastern China suggested that BRI can be used as a surrogate index for evaluating diabetes in the Chinese population [44]. Two other studies in China have also reported that BRI can be used as a surrogate index for predicting insulin resistance and diabetes [45,46]. In this study, BRI was found to be a good predictor of diabetes risk, but it was not the best index. In 2012, Krakauer proposed that ABSI is significantly related to premature death in the general population [10]. Compared with BMI and WC, ABSI has a better ability to predict all-cause mortality, but it is not good at predicting chronic diseases [47]. In various studies, ABSI was not considered the best index for predicting diabetes risk [8,[48][49][50]. Including in this study, although ABSI is better than BMI, it is still not the best predictor of diabetes risk. In 2011, Bergman proposed that BAI has a strong correlation with DXA-derived obesity rates, which can be used to reflect the body fat percentage of adults of both sexes [12]. In our study, although BAI is an independent predictor of diabetes, its predictive power is far lower than that of the other indices, consistent with other studies [6].
In this study, the AUC of various anthropometric indices was greater for women than for men. After our stratification, the number of people in each group was reduced, especially in men, which may have led to the fact that obesity indicators did not show good predictive power in men because of the small sample size. In addition, obesity contributes significantly to diabetes, however, this differs significantly by race/ethnicity and gender [18]. As known, women have higher body fat than men. We therefore speculate that compared with obesity in men, obesity in women is more closely related to diabetes risk. Due to the difference in fat content between men and women and the distribution of fat between different ethnic groups, the best cut-off value for identifying diabetes is not exactly the same in different populations. In our ROC analysis, the ideal BMI thresholds for the Chinese Dong men and women and Miao men and women for identifying diabetes risk were 23.88, 24.58, 26.36, and 26.14, respectively, and the ideal WHR thresholds were 0.94, 0.92, 0.91, and 0.89, respectively. For the Chinese population, BMI � 24.00 is defined as overweight and male WHR � 0.9 and female WHR � 0.8 are defined as central obesity. Xiao et al. reported WHR cut-off values of 0.92 and 0.85 for men and women from the Chinese Han population [6]. In our study, except for the Dong men, BMI cut-off values of the other subgroups were all higher than the national standard. Except among the Miao men, WHR cut-off values of the other subgroups were also significantly higher than the national standard and the Han ethnic group. Therefore, the same critical value should not be adopted between different ethnic groups and genders.
Our study has several limitations. First, this study only included baseline data and could not make causal inferences about the relationship between personal measurement indices and diabetes. Second, data on blood glucose after a 2-h oral glucose tolerance test were lacking, which may have led to the underestimation of diabetes prevalence. Our study also has some advantages. One advantage is that we are focusing on Dong and Miao ethnic groups in China, unlike previous studies that focused on the Han ethnic group. Moreover, our research used ethnic and gender stratification to obtain more targeted results and exclude the influence of gender.

Conclusion
In conclusion, WHR may be the best anthropometric index for predicting diabetes risk. Its cut-off values in Chinese Dong men and women and Miao men were 0.94, 0.92, and 0.91, respectively. Miao women had higher visceral fat than the other three subgroups, and the index WC could not distinguish between SAT and VAT; thus, VAI could better predict diabetes risk in this population, with a cut-off value of 2.20, compared with the other indices. Although obesity is a risk factor for diabetes, it is a preventable and controllable factor. Our findings may contribute greatly to diabetes prevention.
Supporting information S1